Advanced Search
Submit one or more of the following items, and they will be searched along with your query in the search box above.
Any submit button will submit all of the items you have changed.

+ Publication-Date Published in the last:

30 days
60 days
90 days
6 months
12 months
this year
2 years
3 years
5 years
10 years

Or published in the following date range:
From (yyyy/mm/dd - month and day are optional) to ('to' is optional)
+ Full Text
Retrieve articles with hyperlinks to:
full text (either free or subscription)
free full text
subscription full text
no full text link
+ Sort-Order
Sort the retrieved articles by:
relevance
publication date
+ Language And with languages:

+ Species
And for:
Humans
Animals
+ Gender
And for:
Male
Female
+ Age And for these age groups:

Newborn: birth to 1 month
Infant: 1 to 23 months
Preschool child: 2 to 5 years
Child: 6 to 12 years
Adolescent: 13 to 18 years
Adult: 19 to 44 years
Middle aged: 45 to 64 years
Aged: 65+ years
80 and over: 80+ years

+ Title
And for this query matching the titles:
+ Transliterated-Title
And for this query matching the title in original language:
+ Abstract
And for this query matching the abstratcs:
+ Major-Mesh
And for this query matching the MeSH-Major terms:
+ Mesh
And for this query matching any MeSH terms:
+ Journal
And for one or more of these journal abbreviated names:
OR OR
(see title abbreviations)+ Volume
And with journal volume number:
+ Issue
And with journal issue number:
+ Page
And with page number:
+ ISSN
And with ISSN:
+ Publication-Place
And with journal's country of publication:
+ Author

+ Affiliation
And with affiliation to:
+ Has-Abstract
Find MEDLINE records with the abstract status:
has abstract
does not have abstract
include both record types
include both record types but rank higher the records having abstract (the default BML behavior) + PMID
Show me only articles for these PMIDs (PubMed IDs):
+ Semantic-Type And with semantic types:

BioMedLib™ Search Engine takes your query and finds the best responses among millions of biomedical articles in the National Library of Medicine's MEDLINE® database.

How is BioMedLib created?

The design principles of BioMedLib are taken from the research we have done on information retrieval in health and medicine.

We take two databases, MEDLINE and UMLS, and process them heavily till we end up with a representation that is optimal for BioMedLib. Both the MEDLINE and UMLS are open access, and you can have them for free. However the content they hold are in generic format, which may or may not be suitable for a particular purpose.

The "we" includes me, Mir Siadaty, who creates the differentiating features of BML, plus works of a large number of people, such as multiple independent groups of programmers like www.fedoraproject.org www.apache.org etc., multiple projects in the National Library of Medicine, and multiple processing machines at different levels of intelligence (no kidding! We automate our knowledge-engineering tasks to a very large degree using machines that are intelligent enough to do the tasks correctly, fast, and cheap. We observe that similar organizations/companies spend significant money to hire and train large groups of people to do similar tasks, only slower and more error-prone).

How much does BioMedLib cost?

We provide access to BioMedLibfree of charge. However, it does cost a significant amount of money to run and maintain BioMedLib software and hardware.
You can help to sustainBioMedLib by doing one or more of the following:

1. Subscribe to the BioMedLib premium service. It has a low monthly fee. The premium service is faster, and it is ad free.2. Ask your health or medical library and your employer to subscribe to BioMedLib so that you and others affiliated with your institution can use the BioMedLib premium service free of charge.3. Communicate with your colleagues and friends about the positive search experience you are having with BioMedLib.

How to contact BioMedLib?

You can contact us by email, write to us via regular mail, or talk on phone. Also you can leave your comments on our blogs.

BioMedLib Search Engine finds relevant articles that other engines will miss (see an example). If you want to make sure you are not missing any articles that are relevant to your research, then you should check BioMedLib.

Furthermore, BioMedLibsaves you time. BioMedLib sorts the articles so that the most relevant ones show at the top of the list. So you don't have to spend a significant amount of time screening long lists of articles.

Additionally, with BioMedLibyou don't need to learn any specific query syntax language in order to get good results. You can type just the words that represent your question, and BioMedLib will do the rest.

On the other hand, if you know or love to use query languages like the PubMed conventions, BioMedLibsupports all such operators and more; like Boolean operators 'AND' 'OR' 'NOT', exact phrase matching via double quotes, nested parentheses, truncation by *, field search tags like [ti] [ab] [mh] [au]; etc. BioMedLib also provides stepwise MeSH-query-builder, as well as advanced search.

You can read more about the advantages (the differentiating features) of using BioMedLib over other search engines. Also you can read about the central feature of BioMedLib.

What features does BioMedLib Search Engine have?

The central feature of BioMedLib Search Engine is that the user finds "better answers", and finds them "faster", as compared to other search engines.

How does BioMedLib find better answers than other engines?

1.BioMedLib performs a comprehensive analysis of your query. It is like mapping the query to MeSH terms or other controlled vocabularies used by search engines like Ovid/Embase/Pubmed, but 100 times more comprehensive. For example, MeSH has 25 thousand terms (concepts), while BioMedLib's knowledgebase contains over 2 million biomedical concepts, expressed by 20 million unique terms (synonyms). There are many advantages to a better more comprehensive query mapping and expansion; like the user will miss less results due to user's choice of words.

2.BioMedLibtags every instance of 20 million terms (synonyms) that express 2 million biomedical concepts, in each of the 20 million articles of Medline database. This is similar to MeSH terms added to each article by the NLM indexers, but much more comprehensive. BioMedLib uses optimized and automated algorithms that assign the tags without need for human intervention. This automation makes it possbile to retag the whole corpus of the 20 million articles about twice per year, when major vocabularies used in BioMedLib's knowledgebase are updated.
Given MeSH, Mtree and other vocabularies (used by other search engines) improve with each annual edition, when was the last time Ovid/Embase/Pubmed retagged their whole corpus of articles?

3.BioMedLib computes an advanced hybrid relevance score, proprietary to BML, for each article of each query that you submit. By default BioMedLib shows the most relevant articles first, where they have the highest chance to be the "best answers" to your query.
Moreover, you can specify a range of publication dates, within which the articles are then sorted by relevance, thus providing you with both timeliness and relevance.

The factors that are used in computing BioMedLib's relevance score include:

3.1 Semantics: presence of relation in addition to the presence of query words. BioMedLib looks for triplets {Concept1, Relation, Concept2} in each sentence of each article, where Concept1 and Concept2 are from your query. BioMedLib assigns higher relevance score to an article that contains such relation triplets.

3.2 Meaning-based (concept-based) search, besides text-word searching. When you submit a query like 'heart attack', BioMedLib not only searches for the words 'heart' and 'attack', but it also searches for the biomedical concept with ID = C0027051, which is a "Disease or Syndrome", with definition of "gross necrosis of the myocardium, as a result of interruption of the blood supply to the area", which has about 100 synonyms taken from about 150 different vocabularies. Other engines may do similar operation, but they understand only a small fraction of the 2 million concepts that BioMedLib understands. BioMedLib assigns higher relevance score to an article that contains the concept (than an article that contains the query words but in separate and unrelated places).

3.3BioMedLib's relevance score includes all the factors used in a typical TF-IDF (term frequency - inverse document frequency) operation. For example, field-weighting is one of such features, like when "major" MeSH terms of an article matches your query, versus the minor MeSHes, and that article is given a higher relevance score.

Therefore BioMedLib utilizes an optimized hybrid of multiple scoring systems, some unique to BML, to deliver its superior relevance score.

How do users find the best answers faster with BioMedLib?

1.BioMedLib takes each article matching your query, and summarizes it such that you will spend less time when deciding if the article is useful for you (the screening porcess) and if you should go ahead and obtain the full-text of the article.

The text summarization of BioMedLib has three steps:1.1 Selecting sentences of the article that contain your query words;1.2 Selecting sentences that express semantic relation between the query concepts;1.3 Highlighting important relevant terms, and the relations between them;

And you have the option of viewing the whole abstract (via Pubmed link, like in "view PubMed record for the above article (PMID = 15171183)."), and to customize the highlighting process (change color choices, or turning it off completely).

2.BioMedLib takes all the articles matching your query, and sorts them by their relevance to your question. It will then show the most relevant answers at the top of the results. It saves the users significant time when the best answers are gathered together rather than sprinkled across a large set of results, otherwise the user needs to screen tens or hundreds of articles to find the most relevant ones.

BioMedLib provides three sorting methods:2.1 By relevance, the superior BML relevance score;2.2 Sort within a publication date range, and by relevance;2.3 By publication date;

3.BioMedLib provides link-outs to full-texts of the articles. It will automatically resolve and display content of your subscribed resources. And it clearly marks the articles that have open-access free full text (BML marks such hyperlinks by "[Free full-text]").

4.BioMedLib provides multiple alternative output options (besides the abstract, and full text).4.1 Export to citation management software;4.2 Saving the search results in PDF format;4.3 Subscription to RSS feeds of your search;4.4 Sending the search results by email;4.5 Saving the history of the queries you submitted, and their results.

BioMedLibprovides the suggestions, where each has a checkbox. Then you can simply check the meanings you meant (and uncheck the meanings you don't want), and resumbit the clarified query. BioMedLib will then automatically screen all the results and choose the ones that match the meaning you intended (the disambiguation process). This saves the user significant time, as the resulting articles are right on target, and the user does not need to screen hundreds of articles searching for the right sense (the intended meaning) of the word in the query.

BioMedLib's "related articles"

BioMedLib provides three types of the "related articles" feature.

1. The standard version of the "related articles" feautre, as done in other engines. This focuses on a single article as the starting point, and usually is computed off-line where the results are stored and then shown to user when user asks. This is done in BioMedLib by a query like '15380493[related]' where the integer is the PMID of the starting article and the search tag [related] signifies the user is asking to retrieve related articles to the article with PMID 15380493;

2.BioMedLib enables the user to customize the starting point of the "related articles" with one or a few words.
In the real-world, usually the user starts with a query, which retrieves a set of articles. The user reviews the results and finds one of them to be very relevant. Then the user asks the "related artilces" for that very relevant article. However, in the standard version, there is no way to incorporate the additional important information contained in the original query that user started with. In other words, in the standard "related articles" version, no matter what query the user used to arrive at the relevant article, the results of the "related article" action will be exactly the same.BioMedLib allows the user to keep the original query as part of the "related articles" computation, thus provding a more precise view of the starting article that matches user's information needs much better. This is done in BioMedLib by a query like '15380493[related] heart attack treatment' where the integer is the PMID of the starting article and the query words 'heart attack treatment' express the specific view of the article that the user is interested in.

3.BioMedLib enables the user to start the "related articles" with multiple articles. In other words, the user can ask for "related articles" to a set of given articles, rather than a single article.
In BioMedLib a query like '(10881502 17437750)[related]' will retrieve articles that are related to both PMIDs 10881502 and 17437750. You can choose bigger sets of multiple starting articles (the multi-article relatedness). And you can further customize the query by adding one or a few query words that express your view of interest.

BioMedLib's semantic query formulation

If the user is asking for "articles discussing side-effects of meidcations (clinical drugs) in children published in the past 5 years"; how would you do that?
The challenge with queries like the above, is that the user is clearly asking for a concept that contains very many specific instances.
In the above query the user is asking for 'clinical drugs', among others. But the user is not specifying a particular instance of drug. In other words any type of "drug" will qualify for the query. Also, any type of the "side-effects" will qualify for the above query. The user is asking for all concepts with the "semantic type" of drug, and all concepts with semantic type of side-effect.

BioMedLib enables the user to search for 135 semantic types, which in turn are organized hierarchically. For example, the query 'drug[semtyp] injury[semtyp]' retrieves all articles that discuss medications and injury or poisoning (side-effects). You can narrow down the query to a range of publication dates like in 'drug[semtyp] injury[semtyp] 2005:2010[pubdate]'. And you can use MeSH headings to further narrow down to children, 'drug[semtyp] injury[semtyp] 2005:2010[pubdate] (("infant newborn")[mh] or "infant"[mh] or ("child preschool")[mh] or "child"[mh] or "adolescent"[mh])'. This will answer the question posed above.

What are the differentiating features of BioMedLib Search Engine?

Meaning-based search

BioMedLib provides "meaning-based search" in addition to the widely available "keyword search".

In a keyword search, the search engine looks for occurrence of user's words in each document, literally. For example if user submits the query
'coronary attack'
the keyword search will return results that are often dramatically different than when user submits the query
'heart attack' .
In the meaning-based search, the search engine is aware that both queries 'heart attack' and 'coronary attack' point to the same meaning (the same
UMLS
concept with ID of C0027051). Therefore the BioMedLib search engine will find not only documents that match literally to user's query words, but also will find all other documents that
express the same meaning
by using "synonyms" of user's words. This guaranties the user won't miss relevant documents just because of the variation in the words the user used to express the question.

Semantic search

BioMedLib provides "semantic search", on top of the meaning-based search (which in turn is added on top of keyword search).

When you submit a multiword query, of course you like to see documents that contain those words. Besides, the document should have the query words in a "related" fashion.

For example if you submit
'plastic surgery infection'
, then not only the documents should contain the three words, but also the documents should explain a sort of relationships between the words. You don't want a document that talks about infection in one paragraph, and talks about plastic surgery somewhere else and unrelated to the infection; do you?

BioMedLib makes sure documents that express relationship between the concepts of your query, will
get higher
ranks, so that you find them at the top of the first page of results.

Better keyword search

BioMedLib, besides its meaning-based search, provides a more accurate keyword search as well.

For example submit the double-quoted query
"single dose erythromycin"
to PubMed, and PubMed will tell you that there is no article in the MEDLINE having the exact phrase "single dose erythromycin". This is not true; in other words PubMed is missing articles that have the exact phrase "single dose erythromycin".
Try it with BioMedLib
, and you will see articles with PubMed IDs 7081971 and 3335066 actually have the exact phrase.

You can try the IDs
7081971
and
3335066
in PubMed and see that the two articles do exist in PubMed's backend database, but PubMed is unable to find them in response to the exact phrase query "single dose erythromycin" (as of November 2009; and August 2010).

Hassle-free advanced search

The advantages of BioMedLib's meaning-based search and semantic search come with zero extra hassle for the user. You enter your query as usual, and BioMedLib engine will expand and optimize your query automatically, no questions asked.

And if you don't like the idea, just uncheck the "expand the query" box in the "[+] Query is expanded" area; and you will get a simple keyword search!
(Note: sometimes the "[+] Query is expanded" area is denoted as "[+] Clarify the query")

How to search for both relevant and recent articles?

In BioMedLib Search Engine you can sort the articles by 'relevance', or by 'publication date' (try the Detailed-Search > Sort-Order). But how can we get articles that are both relevant AND recent?

1. Start a new BML query;

2. Enter your query into the search box, and submit it. BML will display the articles sorted by relevance (this is the default sorting method in BML);

3. Go to 'Detailed-Search > Publication-Date' and then choose the recent period that fits your need, like the 'Published in the last 6 months', and 'Add' it to the query;

4. Now BML will take all articles matching your query, then narrow it down to the articles published in the 'last 6 months', and then sort the articles by their relevance to your query. Therefore in the first page of results you have the most relevant articles that are all 'recent'.

Therefore, BML not only allows you to sort the retrieved articles by 1.relevance, and by 2.pulbication-date, but BML also has a third choice that allows you to 3.'intersect' the two sorting methods, so that you get articles that are both relevant and timely. Enjoy!

Sorting methods in BML:
1.Sort articles by their relevance to your query;
2.Sort articles by their publication dates;
3.Sort articles by their relevance, within a publication-date range of your choice.

The Quick Way
There is a quick way to get the relevant and recent articles for your query.
After submitting a new query to BioMedLib-which by default sorts the articles by relevance-you will see a few hyperlinks right below the search box:
"Focus on the recent 5 years", "Focus on the current year", "Focus on the last 30 days", etc.
Simply click on the desired hyperlink, and BioMedLib will find the articles that are both relevant and recent.
You can find more choices to define what you consider as recent in the 'Advanced-Search > Publication-Date' menu.

What field-specific searches does BioMedLib Search Engine support?

BioMedLib supports all the fields defined for articles in MEDLINE, ultimately!
The current list of fields that allow search within that specific field, are listed in the 'advanced search' section. Click on the yellow plus sign '[+]' to the left of the menu option 'Advanced search', and the section will expand/appear. You will then see individual fields for searching. When you use them to enter values for specific fields and then submit the query, then BioMedLib will add the values to the query in the main search box, with appropriate field tag in the square brackets '[]'.

Note that if the operand of a search tag has multiple words/parts separated by space, then you should use parentheses to enclose them and then add the search tag, like in (sudden infant death)[mh] .

What is BioMedLib query expansion?

When you ask for 'heart attack', BioMedLib Search Engine will find articles that are about 'heart attack'; besides, BioMedLib will find articles that have equivalent terms. Such equivalent terms are called 'synonyms'. For example, 'myocardial infarction', 'coronary attack', and 'heart infarction' are a few synonyms for the query 'heart attack'.

Expanding your query to its synonyms will decrease the possibility that any relevant article is left out (false negatives). BioMedLib will automatically do it for you. You don't need to type in all the synonyms, or even recall them.

Click on the plus sign '[+]' of the menu option 'Query is expanded' or 'Clarify the query', and it will expand. You will then see terms in your query that are mapped to synonyms. There are 'details' hyperlinks that take you to a page where more information (such as definition, semantic type, and other synonyms) for your term is provided.

What is BioMedLib query disambiguation?

Sometimes words or terms in your query may have more than one meaning. Then BioMedLib Search Engine will remind you by showing a red plus sign '[+]' with the menu option 'Clarify the query'. When you click on the plus sign and expand the section, you will see the term in your query that has multiple meanings. Each meaning has a checkbox. Uncheck the meanings you are not looking for, and click the section's submit button. For example, for the query 'peg complications', BioMedLib shows that there are multiple meanings for 'peg'. Some of the meanings are specific chemicals such as 'polyethylene glycol', while another meaning is 'percutaneous endoscopic gastrostomy' which is a therapeutic procedure.

When you choose a specific meaning, and click the submit button, then BioMedLib screens the articles for the precise meaning you requested. This should save you some significant time. It might have taken you many minutes or even hours to screen out the articles with the unwanted meaning, but BioMedLib will do it for you in a few seconds.

If you don't want the BioMedLib's query expansion, simply uncheck the 'Expand the query' box, and resubmit the query.
If you encounter a term where BioMedLib's automatic mapping is unsatisfactory, please send us a note via email.

How does BioMedLib Mesh-query-builder work?

The BioMedLib Mesh-query-builder is useful if you want to find and use Mesh terms when searching for articles.
Let's assume you want to search for articles about 'heart attack', but then want to use the Mesh term for 'heart attack' to do the search.

2.Type in your query (the 'heart attack' in this example), and click the 'Find Mesh' button. The BML will find up to 30 Mesh terms that best match your query, and present them to you, each hyperlinked.

3.Look at the list of retrieved Mesh terms, and locate the one that conveys your query best (for the example of 'heart attack', it is the first choice in the list, 'myocardial infarction').

4.Click on your chosen Mesh term; and BML will use it as the query to find the best articles. BML will then display the articles for you.

5.Even though BML has already displayed the articles for the Mesh term you have chosen, BML shows you Mesh Subheadings besides the articles. You then have the choice to further prune the collection of the retrieved articles, by clicking on a Mesh Subheading from the hyperlinked list (which contains about 80 of them).

If you have had some query in the main search box, before you started the Mesh-finder query, then BML will add the Mesh term that you chose to the previous query. One advanatage is that you can add multiple Mesh terms consecutively (one at a time) by following the steps explained above, iteratively. If you don't want previous query to be added to your Mesh term, then first click on the 'BioMedLib.com' logo at the top of the page (go to homepage), and then start using the BML Mesh query builder.

Note that BML creates the query for you and displays it in the main search box, based on the Mesh term, and possibly the Mesh Subheading, which you have chosen. You will see that BML wraps the Mesh term in parentheses before adding the search tag, like in '(myocardial infarction)[mh]' or in '(myocardial infarction/prevention and control)[mh]'. This is the syntax that BML expects to see.

How to change or turn off the highlighting of article words?

BioMedLib uses two hues of red (or other colors of your choice) to help you quickly locate your query words (bold hue), and their synonyms (light hue). However, if you prefer the words not to be highlighted, then click the 'Advanced search' option in the menu to expand it, and then scroll down to the subsection '[highlight color]'. There you have multiple choices of color, as well as no highlight.

What is the 'summary' section in the BioMedLib Search Engine results?

BioMedLib tries to save your time, and help you to quickly decide if an article is worth seeking the full-text. It will do so via multiple steps of text summarization.

BioMedLib will show sentences from the article's title, abstract, and mesh, which contain your query words, or equivalents (synonyms) of the query words. The query words are highlighted, usually in red. The synonyms are highlighted with a lower degree.

When there are multiple biomedical concepts in your query, BioMedLib tries to find sentences that not only have your query words, but also declare a type of relationship between the concepts you are searching for. Usually these are key sentences that help you to decide if an article is really relevant for your question, and decide if you want to get the full-text. The 'Summary' section in the results page contains these sentences. Soemtimes this is called 'semantic search'.

How to get the full-text article?

Click on the [Fulltext service] link below the article you want. It will take you to the full-text service page.